Method and system for projecting image with differing exposure times

ABSTRACT

A method for creating a projected image including capturing an image of an item or data relating to an image of an item with an image capture device having a plurality of light sensing units, at a first exposure time. The method further includes receiving as an input or determining a second exposure time, and creating a projected image of the item at the second exposure time based upon the captured image or the captured data relating to the image.

RELATED APPLICATIONS

This applications claims priority to U.S. application Ser. No.61/818,107 filed May 1, 2013 which is expressly incorporated byreference herein in its entirety.

A method and system for projecting an image with differing exposuretimes, such as for use with images generated from a charge coupleddevice (CCD) or the like.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 is a flowchart showing one embodiment of the method and system.

FIG. 2 is an image of an object at an initial exposure time.

FIG. 3 is a projected image of the object of FIG. 2 at an exposure timecalculated to be optimal.

FIG. 4 is a projected image of the object of FIG. 2 at an exposure timelonger than that of FIG. 3.

FIG. 5 is a projected image of the object of FIG. 2 at an exposure timeshorter than that of FIG. 3.

FIG. 6 shows various projected images, at a 10 minute exposure time, ascompared to an actual image at a 10 minute exposure time.

FIG. 7 shows an image of an object in front of a background.

FIG. 8 schematically illustrates a general purpose computer systemsuitable for operating the method and system disclosed herein.

Life science researchers routinely obtain images of mixtures ofmacromolecules, such as DNA, RNA and proteins, and their fragments, fromstained gel electrophoresis samples and Western blots. The images arethen captured and analyzed to obtain data.

In order to separate the complex mixtures using electrophoresis, severalsamples containing the mixture are applied to separate, spaced apartlocations on the electrophoresis gel. An electrical current is thenapplied to the gel, which causes the individual samples to migratethrough the gel within their prescribed lane or track, therebygenerating an invisible lane on the gel. The complex mixture is thenseparated by size, i.e., molecular weight, and net charge in the gelmatrix. The larger, higher molecular weight with low net chargemolecules remain relatively nearer the place of sample loading on thegel or membrane. The smaller, lower molecular weight molecules with highnet charge migrate farther from the sample loading place of the gel ormembrane. Each individual segregation of sample is then identified as aband. The gel can then be stained for total sample visualization, ortransferred to a membrane for visualization of a specific target ofinterest by blotting (Western blotting in the case of proteins, Southernblotting in the case of DNA, and Northern blotting in the case of RNA).The researcher then images the gel, membrane or blot, collectivelytermed a substrate or object, to analyze the target(s) of interest foramount, relative or absolute, purity, and molecular weight. Suchanalysis requires detection and identification of the lanes and bands inthe image.

The images of the object are typically acquired using one or morevisualization means, such as ultra violet light illumination, whitelight illumination, fluorescence, or chemiluminescence.

Finding proper exposure time is an important factor affecting imagequality and it is important for successful, accurate pixel intensitymeasurement on the acquired image. Various auto exposure methods havebeen developed, but those methods are either complex, inaccurate and/ordisregard user input. Optimal exposure time for image capture is notalways dependent on pixel intensity of the entire image or anyparticular region of the image. In some cases, the user/operator canbest define which object(s) on the captured image should be the targetfor optimal exposure determination.

Images of the substrate, or of other objects, can be captured by any ofa wide variety of structures or devices, but in one case takes the formof an imaging device or image capture device utilizing/comprising a CCD(Charge Coupled Device), but can also be used with autoradiography,scanners, CMOS (Complementary Metal Oxide Semiconductor) imagers,phosphor imagers, and others. In the case of the CCD, such a systemutilizes an array of light-sensitive optical elements, such as pixels orother light sensing units. The pixels are configured such that whenlight (photons) are detected by the pixels, each pixel provides anoutput in the form of an electrical signal that is proportional orrelated to the intensity of the detected light (photons). Multiplepixels or arrays of pixels can also be combined together using thewell-known binning technique, and in this case each group of binnedpixels can be considered a single pixel.

Each pixel has a limited capacity for maximum light exposure, also knownas its saturation point. If too many pixels reach their saturation pointfor a given image, the image is considered over-exposed. In contrast, iftoo many of the pixels receive insufficient light, the image lackssufficient contrast and is considered under-exposed. Thus, whencapturing images it is helpful to determine the optimal exposure time sothat data from the image can be accurately captured. Use of the optimalexposure time maximizes the dynamic range of the pixel intensities inthe image, and minimizes the number of pixels that are saturated.

In previous systems, in order to determine the proper exposure time forimage acquisition, a trial-and-error image acquisition process or acomplex, inaccurate automatic exposure method was utilized. Theuser/operator would need to carry out multiple image acquisitions withdiffering exposure times, compare the images, and make estimates as tothe best exposure time. However this process is labor-intensive and alsotakes up usage of the imaging equipment that would otherwise be put toproductive use.

Thus, in one embodiment the present invention provides a system andmethod for providing, and/or enabling a user/operator to determine theoptimal exposure time for image acquisition based upon only a singleimage acquisition. In order to carry out this method and system, andwith reference to FIG. 1, a test or preliminary image of the substrateor object is first obtained under a test, or first, exposure time. Thetiming/value of the test exposure time can vary as needed, and accordingto the specific equipment and nature of image expected to be acquired.However, the test exposure time in many cases is shorter than the normalor expected full or optimal exposure time. In one case, for example, thetest exposure time is between about 10 milliseconds and about 10minutes, or in another case between about 1 second and about 60 seconds,or more particularly between about 5 seconds and about 25 seconds, or inone case less than about 60 minutes, or in another case less than about1 minute.

After the image (test image) at the test exposure time is acquired,noise subtraction algorithms (including accounting for dark noise, biasnoise, flat field, etc.) are applied to the data/image in a well-knownmanner. The signal intensity, or output, of each pixel can then bearranged/ordered. The system then analyzes the number of pixels thatexceed a threshold value (and/or are projected to exceed a thresholdvalue), and uses that number to determine the optimal exposure time.

By way of example, in a 16 bit image system, the output of each pixelcan be an integer ranging from 0 to 65,535. Of course, 8 bit, 32 bit,gray or color or other imaging systems can be utilized, and the outputvalues of each pixel can therefore vary widely. The system can thenanalyze the number of pixels that are at saturation value (e.g. at65,535 for a 16 bit image) or at some value close to saturation (e.g.90% of saturation in one case, at a value at or exceeding 58,982), orsome other threshold value. The system will have pre-programmed into it,or stored therein or provided thereto, a number representing the numberof pixels or a percentage of pixels which should be at or above thethreshold value to provide the desired/optimal image. For example, inone case it may be known that a best image can be expected to beprovided if 5% of the pixels are at or near saturation or above thethreshold value. Alternately, rather than considering a percentage ofpixels, the system may instead analyze the raw number of pixels that areat or near saturation or above the threshold value.

Thus, in the case of a 2.1 megapixel CCD array, and continuing to usethe 5% number as an example, the system may use a number of 0.105megapixels at the cut-off which are desired to be above the thresholdvalue. Of course, the cut-off percentage and cut-off number of pixelscan vary widely depending upon the type of image desired/expected, theproperties of the equipment, etc. In addition, it should be understoodthat rather than utilizing the cut-off number at this point, projectedvalues for each pixel can be generated, and then a threshold is applied,and/or the optimal exposure time calculated in other manners.

As mentioned above, in addition to using a percentage of pixels that canbe at or above the threshold, a raw number of pixels can be used forthis purpose. To determine the raw number of pixels that can be at orabove the threshold, various methods can be utilized including but notlimited to: A) analyzing the number of pixels typically encompassed byan object of interest on an image at different binning levels,resolutions, etc. and/or B) analyzing the number of pixels that reachsaturation at different binning levels, resolutions, etc. using themaximum exposure time when no object is imaged (i.e., background suchthat only uncompensated random noise is present in the image).

It can be assumed that the intensity value for each pixel will increaselinearly/proportionally, or at some known non-linear rate, with respectto increased exposure time. Thus once the data for the test image isknown, and the threshold value for high intensity pixels are known, theoptimal exposure time for the image can be calculated. Continuing withthe example set forth above and assuming a linear relationship betweenpixel intensity and exposure time, it can be seen that if it is desiredthat the image have 0.105 megapixels of its 2.1 pixels be close tosaturation (at or exceeding a threshold value of 58,982), the originalexposure time (say, 15 seconds) should be multiplied by a number, whichneeds to be determined, to provide the desired output. For example, itmay be determined that if the test exposure time is increased 4 times,0.105 megapixels of the test image will be at and/or exceed thethreshold value, resulting in an optimal exposure time of 4×15 seconds,or 60 seconds.

In one case, then, the optimal exposure time can be calculated by: 1)determining the number of pixels that are desired to exceed thethreshold value; 2) from the test image data conducted at a test imageexposure, selecting the number of pixels, from step 1, of pixels withthe greatest intensity; 3) from the group of pixels defined in step 2)selecting the pixel with the smallest intensity value; 4) dividing theintensity value from step 3) by the time of the test image exposure; and5) dividing threshold value by the numerical result of step 4),resulting in the optimal exposure time.

By way of example, consider the following 16 bit image data from asimple eight pixel array, which represents pixel output received after a15 second exposure time:

TABLE 1 Pixel Number Pixel Output 1 5,000 2 10,000 3 15,000 4 20,000 525,000 6 30,000 7 35,000 8 40,000

In this case, let it be assumed that it is desired that 25% of thepixels exceed a cut-off value of 58,982. In this case, then, under step1 above it is determined that two pixels, or 25% of the eight totalpixels, are desired to exceed the threshold value. Under step 2 fromabove, the two pixels of highest intensity are selected, which arepixels number 7 and 8. Under step 3, the pixel with the smallestintensity value between pixels 7 and 8 (pixel 7) is selected. Theintensity value for pixel 7 (35,000) is then divided by the test timeexposure (15 seconds) resulting in a value of 2333. The cut-off value(58,982) is then divided by 2333, resulting in a value of about 25.3seconds. 25.3 seconds can then be considered the projected optimalexposure time.

Once the optimal projected exposure time is determined, a projectedimage can be generated, projecting/extrapolating how the image will lookbased upon the test image data, and assuming that intensity variesdirectly with exposure time (i.e. assuming a linear, or a knownnon-linear relationship between intensity and exposure time). Continuingwith the example above, the pixel output of Table 1 will be multipliedby 25.3/15 resulting in the following output:

Pixel Number Pixel Output 1  8,433 2 16,867 3 25,300 4 33,733 5 42,167 650,600 7 59,033 8 65,535* (67,466) *In this example, values that exceedthe maximum intensity for a pixel (65,535) are assigned the maximumintensity value (16 bit image)

In this case, then, it can be seen that 25% of the pixels exceed thepixel threshold of 58,982, as desired. Of course, there are a widevariety of mathematical methods and algorithms and pixel math techniquesthat can be utilized to determine an exposure time that is projected toprovide a minimum number/percentage of pixels that exceed a thresholdvalue, and the technique outlined above is simply one example. Thesystem and method specified and claimed herein is not limited thespecific technique shown above. In any case, once the projected optimalexposure time image is calculated, the projected image can be generatedand presented to the user/operator.

FIG. 2 illustrates one example of a test image at a test exposure of tenseconds in the illustrated embodiment, as shown in the exposure timedisplay 10. In this figure the image is slightly underexposed. Thecalculations outlined above can be applied to the image/image data ofFIG. 2, resulting in a projected optimal exposure time of 17 seconds.FIG. 3, then, shows an image based upon the data/image of FIG. 2 aspresented to a user at a projected optimal exposure time, or a secondexposure time. In this case the projected optimal exposure time is 17seconds, as calculated by the system in the manner outlined above.

The system may also provide the option to a user to manually adjust theexposure time (i.e. via a user input device, resulting in a third oruser-created exposure time), and the system can adjust the projectedimage accordingly. In other words, each pixel output can be adjusted ina manner directly proportional to the input exposure time to present animage to the user/operator, so that the user/operator can see how theimage is projected to look at a user-defined exposure time. In theillustrated embodiment, the input device takes the form of slider bar 12that can be adjusted by a user via a cursor control device of the like.In one case, the projected image 14 is displayed in real time to matchthe position of the slider bar 12 as it is moved so that theuser/operator is provided with instantaneous feedback.

When the slider bar 12 is utilized, the numerical value of the exposuretime is displayed in the exposure time display 10. Alternately, or inaddition, the user/operator may be able to directly enter the numericalvalue of the desired exposure time to be displayed, or control thenumerical values with navigation buttons 16, etc.

Thus, while the system can generate a projected optimal exposure time,it is understood that in some cases the user/operator may desire anexposure time different from the calculated optimal exposure time, basedupon the user/operator's review of the displayed projected image. FIG. 4presents a projected image 14 where the user/operator had increased theexposure time (to 5 minutes, in the illustrated embodiment) to the pointwhere the image 14 is over-exposed. FIG. 5 presents a projected imagewhere the user/operator had decreased the exposure time (to 8 seconds,in the illustrated embodiment) to the point where the image 14 isunder-exposed.

The test/initial exposure time can be selected to provide an accuratebaseline image for use in projecting images and/or determining optimalexposure times, while also providing convenience for the user. Inparticular, the longer the test/initial exposure time, the more accuratethe (longer exposure time) projected image(s) will be. FIG. 6illustrates estimated images for a ten minute exposure image, based upontest/initial exposure times of 10 seconds, 15 seconds, 20 seconds, and30 seconds. FIG. 6 also illustrates an actual image taken at 10 minutes.As can be seen, the longer the initial exposure time (or closer to theactual exposure time), the more accurate the projected image. On theother hand, making the initial exposure time too long can take up timeand resources.

The user can modify the exposure time and/or accept the projectedoptimal exposure time as presented by the system, to select/define theoptimal/desired exposure time. Once the optimal/desired exposure time isselected/defined, an image can be acquired at the selected/definedexposure time and used for further processing. The actual image acquiredat the optimal or selected exposure time will, of course, vary from theprojected/preview images as outlined above. In particular, in theprojected/preview images based upon the test image, the noise levelswill be disproportionally increased as compared to the actual imageacquired at the optimal/selected exposure time. Therefore, further noisereduction processes can be applied to create clean projected/previewimages. However the system and method enables optimal exposure time tobe determined/selected using, in one case, only a short, preliminaryimage acquisition time. The test image may not, in some cases, providevisual data to a human viewing the image, but may provide sufficientinformation after analysis to provide the benefits outlined above. Thesystem and method can be used with nearly any light imaging system, butmay find particular utility with detecting low light objects.

In one case, the creation and display of projected images at differingexposure times, and/or the determination of optimal exposure time, canbe limited to only a certain area or portion of the test/initial image.In particular, in some cases the test/initial image may include theentire substrate or object, along with a background area. The object andbackground area may be present a relatively high contrast when imaged.For example, the object may be generally white or light, and thebackground may be generally black or dark. In this case, or in othercases where possible, it may be advantageous to distinguish the objectfrom the background area, and apply the techniques described herein onlyto the object/substrate.

For example, FIG. 7 shows an image of a light substrate/object in frontof a dark background. After this test/initial image is acquired, thesystem/method may determine the outline of the object. Due to the highcontrast between the object and the background of FIG. 7, any of a widevariety of edge-locating or contrast-locating algorithms may be utilizedto determine the boundary between the object and background. In theillustrated embodiment the corners of the object are located usingwell-known corner-locating algorithms, and the edges of the objectdetermined by projecting straight lines between the corners. Of course,the object can have various other shapes besides rectangular, in whichcase other suitable algorithms are utilized to determine the outer edgesof the object.

Once the shape and dimensions of the object are determined, all datarelating to areas outside the object can be ignored, and not form thebasis for any further image projecting or optimal exposure timedeterminations as outlined above. For example, in one case each pixeldetermined to be outside the object is set to an arbitrary value (0, inone example), or each pixel simply remains at its value from theinitial/test image. In either case, the exposure processing outlinedabove is carried out only on the image pixels determined to be on/withinthe object, which can significantly reduce amount of pixels to beprocessed. This process thereby enables more rapid calculations,providing a quicker response time and saving computing resources.

Thus, as outlined above, only a portion of the originally-acquiredimage, or a region of interest (“ROI”), may be utilized for the creationand display of projected images at differing exposure times, and/or thedetermination of optimal exposure time. In the illustrated embodiment,the entire substrate/object forms the ROI. However, the ROI canconstitute various-other areas, such as particular areas of interest inthe substrate/object.

Embodiments of the invention include a method, data processing systemand/or computer program product. Thus, one embodiment is entirelyhardware with logic embedded in circuitry, one embodiment is entirelysoftware with logic operating on a general purpose computer to performthe method and operate the system, and/or one embodiment combinessoftware and hardware aspects. One embodiment takes the form of acomputer program product on a tangible, non-transitory computer-readablestorage medium having computer readable program code means embodied inthe medium. Any suitable computer readable medium may be used includinghard disks, CD-ROMs, optical storage devices, static or nonvolatilememory circuitry, magnetic storage devices and the like. The executableprogram may be available for download from a website.

FIG. 8 shows an exemplary computer or computing system 100 that can beused to implement the method and system. The computer system 100 can bea laptop, desktop, server, handheld device (e.g., personal digitalassistant (PDA), smartphone, tablet), programmable consumer electronicsor programmable industrial electronics.

As illustrated the computer system 100 includes a processor 102 that canbe any various available microprocessor(s). For example, the processorcan be implemented as dual microprocessors, multi-core and othermultiprocessor architectures. The computer system 100 includes memory104 that can include volatile memory, nonvolatile memory or both.Nonvolatile memory can include read only memory (ROM) for storage ofbasic routines for transfer of information, such as during computer bootor start-up. Volatile memory can include random access memory (RAM). Thecomputer system 100 can include storage media 106 including, but notlimited to, magnetic or optical disk drives, flash memory, and memorysticks.

The computer system 100 can incorporate one or more interfaces,including ports 108 (e.g., serial, parallel, PCMCIA, USB, FireWire) orinterface cards 110 (e.g., sound, video, network, etc.) or the like. Inembodiments, an interface supports wired or wireless communications.Input is received from any number of input devices 112 (e.g., keyboard,mouse, joystick, microphone, trackball, stylus, touch screen, scanner,camera, satellite dish, another computer system and the like). Thecomputer system 100 can output data through an output device 114, suchas a display (e.g. CRT, LCD, plasma), speakers, printer, anothercomputer or any other suitable output device.

The description above references flowchart illustrations of methods,apparatus (systems) and computer program products. It will be understoodthat each block of the flowchart illustrations, and combinations ofblocks in the flowchart illustrations, and/or part thereof, can beimplemented by computer program instructions. These computer programinstructions may be loaded onto a computer or other programmable dataprocessing apparatus or otherwise encoded into a logic device to producea machine, such that the instructions which execute on the computer orother programmable data processing apparatus create means forimplementing the functions specified in the flowchart block or blocks.These computer program instructions may also be stored in a computerreadable memory that can direct a computer or other programmable dataprocessing apparatus to function in a particular manner, such that theinstructions stored in the computer readable memory produce an articleof manufacture including instruction means that implement the functionspecified in the flowchart block or blocks. The computer programinstruction may also be loaded onto a computer or other programmabledata processing apparatus to cause a series of operational steps to beperformed on the computer or other programmable apparatus to produce acomputer implemented process such that the instructions which execute onthe computer or other programmable apparatus provide steps forimplementing the functions specified in the flowchart block or blocks.

Specific functional blocks, or parts or combinations thereof, presentedin relation to the disclosed methods and systems are programmable asseparate modules or functional blocks of code. These modules are capableof being stored in a one or multiple-computer storage media in adistributed manner. In one embodiment, these modules are executed toperform the method and system in whole or in part on a single computer.In one embodiment, these modules are executed to perform the disclosedmethods and systems on multiple computers that cooperatively execute themodules. In one embodiment, the programs are executed in a virtualenvironment, where physical hardware operates an abstract layer uponwhich the disclosed methods and systems are executed in whole or in partacross one or more physical hardware platforms.

In addition, it should be understood that the system and methoddisclosed herein is not limited for use with imaging substrates orobjects after electrophoresis, and indeed is also not limited to use inthe life sciences field. Instead, the system and method can be used innearly any imaging system in which it is desired to create a projectedimage and/or determine an optimal exposure time.

The embodiments described in the specification are only specificembodiments of the inventors who are skilled in the art and are notlimiting. Therefore, various changes, modifications, or alterations tothose embodiments may be made without departing from the spirit of theinvention or the scope of the following claims.

What is claimed is:
 1. A method for creating a projected imagecomprising: capturing only a single image of a biological sample with animage capture device having a plurality of light sensing units, at afirst exposure time; analyzing the captured image of the biologicalsample to determine the value of pixels included in the captured imagethat are within a threshold level of pixel value, wherein the thresholdlevel of pixel value is a value of a pixel that contributes to anon-saturating optimal image of the biological sample; determiningwhether the value of pixels included in the captured image that arewithin the threshold level of saturation, is within a threshold value ofpixels, wherein the threshold value of pixels is a value of pixels thatdo not exceed the level of saturation of a pixel to generate anon-saturating optimal image of the biological sample; calculating anoptimal exposure time by adjusting the first exposure time to increasethe value of pixels included in the captured image linearly to have thevalue of a pixel that satisfies the threshold level of pixels so thatthe value of pixels include in the non-saturating optimal image arewithin the threshold level of optimal pixel value satisfying thethreshold value of pixels to generate the non-saturating optimal imageof the biological sample; creating the non-saturating optimal image withthe calculated optimal exposure time to generate the projected imagewith the value of the pixels included in the projected image thatsatisfies the threshold level of pixel value; and wherein saiddetermining step includes determining said optimal exposure time bycarrying out the following steps: determining a signal intensity foreach light sensing unit based upon said single captured image;determining a number or percentage of said light sensing units thatexceed a first pixel value; determining a multiplier of said firstexposure time which will cause said number or percentage of lightsensing units to exceed a threshold pixel value; and applying saidmultiplier to said first exposure time to arrive at said optimalexposure time.
 2. The method of claim 1, wherein each light sensing unitprovides an output relating to the biological sample, and wherein thecreating step includes creating the non-saturating optimal image by analgorithm based upon the output of the light sensing units.
 3. Themethod of claim 2, wherein the creating step includes creating thenon-saturating optimal image generated by the algorithm by extrapolatingthe output of the light sensing units.
 4. The method of claim 3, whereinthe extrapolating algorithm is based upon an assumption of a linear or aknown non-linear relationship between the output of the light sensingunits and exposure time.
 5. The method of claim 1 wherein said optimalexposure time is greater than said first exposure time.
 6. The method ofclaim 1 wherein the first exposure time is less than a minute.
 7. Themethod of claim 1 further comprising capturing a supplemental image ordata of the biological sample with the image capture device at acalculated second exposure time.
 8. The method of claim 1, furthercomprising receiving user input of a second exposure time, and creatingan algorithm generated second image of the biological sample at saiduser input second exposure time based upon said single captured image ofthe biological sample.
 9. The method of claim 1, wherein the projectedimage is generated by algorithm, the method further comprisingdisplaying said algorithm generated projected image.
 10. The method ofclaim 9, further comprising: receiving a user input exposure time,determined based upon user review of the displayed algorithm generatedprojected image; and capturing a supplemental image at the user inputexposure time.
 11. The method of claim 1 wherein the biological sampleis a substrate including electrophoretic bands thereon.
 12. The methodof claim 1 further comprising after the capturing step, identifying aregion of interest in said single image, and wherein the creating stepincludes creating an algorithm generated projected image based on theexposure time calculation from only said region of interest.
 13. Themethod of claim 12 wherein the single image comprises an item or a data,wherein the item is an object and a background, and wherein the regionof interest is the object.
 14. The method of claim 12 wherein the regionof interest is at least partially identified by an edge-detectingalgorithm.
 15. The method of claim 12 wherein the region of interest isinput by the user.
 16. The method of claim 1 further comprisingdetermining a second exposure time utilizing a portion of theoriginally-captured image, or an area of interest in the captured imageat a first exposure time.
 17. The method of claim 1 further comprisingdetermining a second exposure time utilizing a user-defined portion ofthe image.
 18. A computer program product comprising: a non-transitorycomputer readable storage medium; and instructions stored on thenon-transitory computer readable storage medium that, when executed by aprocessor of an image capturing device having a plurality of lightsensing units, cause the image capturing device to: capture only asingle initial image of a biological sample relating to an image of thebiological sample at a first exposure time; analyzing the initial imageof the biological sample to determine a value of pixels included in theinitial image that are within a threshold level of pixel value, whereinthe threshold level of pixel value is value of a pixel that contributesto an optimal image of the biological sample; determining whether thevalue of pixels included in the initial image that are within thethreshold level of pixel value is within a threshold value of pixels,wherein the threshold value of pixels is a value of pixels that does notexceed the threshold level of pixel value to generate an optimal imageof the biological sample; calculating an optimal exposure time byadjusting the first exposure time to increase the value of pixelsincluded in the initial image to have the level of pixel value thatsatisfies the threshold level of pixel value so that the value of pixelsincluded in the optimal image that are within the threshold level ofpixel value satisfies the threshold value of pixels to generate theoptimal image of the biological sample; creating an optimal image at theadjusted exposure time to generate the optimal image with the value ofpixels included in the image that satisfy the threshold level of pixelvalue; and generating a projected image that can be presented to a useror operator; and wherein said determining step includes determining saidoptimal exposure time by carrying out the following steps: determining asignal intensity for each light sensing unit based upon said singlecaptured image; determining a number or percentage of said light sensingunits that exceed a first pixel value; determining a multiplier of saidfirst exposure time which will cause said number or percentage of lightsensing units to exceed a threshold pixel value; and applying saidmultiplier to said first exposure time to arrive at said optimalexposure time.
 19. An image capture system comprising: an image capturedevice having a plurality of light sensing units configured to captureonly a single image a biological sample at a first exposure time; and acomputing system operatively coupled to said image capture device andconfigured to: analyze the single captured image of the biologicalsample to determine a value of pixels included in the single capturedimage that are within a threshold level of pixel value, wherein thethreshold level of pixel value is a value of a pixel that contributes toan optimal image of the biological sample, determine whether the valueof pixels included in the single captured image are within the thresholdlevel of pixel value is within a threshold value of pixels, wherein thethreshold value of pixels is a value of pixels that does not exceed thethreshold level of pixel value to generate an optimal image of thebiological sample, adjust the first exposure time to increase the valueof pixels included in the single captured image to have the energy levelthat satisfies the threshold level of saturation so that the value ofpixels included in the single captured image that are within thethreshold level of saturation satisfies the threshold value of pixels tocalculate an optimal exposure time, to generate the optimal image of thebiological sample, create the optimal image with the calculated optimalexposure time, and generate a projected image which can be presented toa user or operator, wherein the computing system has firmware or atleast one processor to execute logic instructions associated with atleast one computer software program, a memory for storing logicinstructions, and user interface adapted to communicate the projectedimage; and wherein said determining step includes determining saidoptimal exposure time by carrying out the following steps: determining asignal intensity for each light sensing unit based upon said singlecaptured image; determining a number or percentage of said light sensingunits that exceed a first pixel value; determining a multiplier of saidfirst exposure time which will cause said number or percentage of lightsensing units to exceed a threshold pixel value; and applying saidmultiplier to said first exposure time to arrive at said optimalexposure time.